Senior ML Engineer

What you'll do

• Build and maintain low-latency, real-time inference services for heavy security data streams.
• Deploy sophisticated models including graph analytics and supervised learning into production environments.
• Implement MLOps methodologies such as automated CI/CD pipelines and robust model performance monitoring.
• Architect internal tooling, feature stores, and libraries to enable rapid scaling of machine learning services.
• Design production services with an attacker's mindset to defend against model evasion and high-volume loads.
• Develop scalable Python code and APIs for real-time inference and distributed processing.
• Manage automated ML pipelines using containerization and workflow orchestration tools like Kubernetes and Airflow.

What we're looking for

• 3-5+ years of experience in machine learning engineering or data science.
• 2+ years of experience deploying anomaly detection and clustering systems in a production cybersecurity environment.
• Extensive hands-on experience with Apache Kafka, Flink, Ray, and Spark/Pyspark for streaming and distributed processing.
• Demonstrated success implementing MLOps methodologies including automated CI/CD, deployment, and real-time performance monitoring.
• Deep understanding of Docker, Kubernetes, and Apache Airflow for managing production-grade ML pipelines.
• Mastery of Python programming with an emphasis on scalable code design and API development for real-time inference.
• Experience building and deploying ML models to production and collaborating with ML research teams.
• Masters or PhD in a quantitative field (preferred).